Abstract
As Generative AI (GenAI) capabilities expand, understanding how to preserve and develop human expertise while leveraging AI’s benefits becomes increasingly critical. Through empirical studies in two contexts — survey article authoring in scholarly research and business document sensemaking — we examine how domain expertise shapes patterns of AI delegation and information processing among knowledge workers. Our findings reveal that while experts welcome AI assistance with repetitive information foraging tasks, they prefer to retain control over complex synthesis and interpretation activities that require nuanced domain understanding. We identify implications for designing GenAI systems that support expert cognition. These include enabling selective delegation aligned with expertise levels, preserving expert agency over critical analytical tasks, considering varying levels of domain expertise in system design, and supporting verification mechanisms that help users calibrate their reliance while deepening expertise. We discuss the inherent tension between reducing cognitive load through automation and maintaining the deliberate practice necessary for expertise development. Lastly, we suggest approaches for designing systems that provide metacognitive support, moving beyond simple task automation toward actively supporting expertise development. This work contributes to our understanding of how to design AI systems that augment rather than diminish human expertise in document-centric workflows.
Sample
“AI systems should preserve expert agency over critical analytical tasks. Experts consistently preferred to retain control over activities that built on the extracted information, such as complex synthesis and interpretation which often required nuanced understanding of values and language semantics. This suggests AI systems should complement rather than replace expert judgment in these areas. For example, survey authors emphasized that once a meaningful narrative or framework has been identified by expertise, the subsequent tasks to fill-in the information are more prone to be delegated.”
Professor at George Washington University
I am a Professor with Human-Technology Collaboration and Educational Technology programs at George Washington University in Washington DC. I have written 12 books and more than 100 articles, and I co-host of the Parsing Science podcast where scientists tell the stories behind their research. I am also the developer of the WeShareScience.com online platform for sharing research videos, and SciencePods.com where researchers can create free podcasts about their science. My research interests include human interactions with intelligent machines, needs, needs assessments, and instructional design.
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